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During the last few years, the amount of o … During the last few years, the amount of online descriptive information about places and their dynamics has reached reasonable dimension for many cities in the world. Such enriched information can now support semantic analysis of space, particularly in which respects to what exists there and what happens there. We present a methodology to automatically label places according to events that happen there. To achieve this we use Information Extraction techniques applied to online Web 2.0 resources such as Zvents and Boston Calendar. Wikipedia is also used as a resource to semantically enrich the tag vectors initially extracted. We describe the process by which these semantic vectors are obtained, present results of experimental analysis, and validated these with Amazon Mechanical Turk and a set of algorithms. To conclude, we discuss the strengths and weaknesses of the methodology. Copyright 2010 ACM.es of the methodology. Copyright 2010 ACM.

During the last few years, the amount of o … During the last few years, the amount of online descriptive information about places and their dynamics has reached reasonable dimension for many cities in the world. Such enriched information can now support semantic analysis of space, particularly in which respects to what exists there and what happens there. We present a methodology to automatically label places according to events that happen there. To achieve this we use Information Extraction techniques applied to online Web 2.0 resources such as Zvents and Boston Calendar. Wikipedia is also used as a resource to semantically enrich the tag vectors initially extracted. We describe the process by which these semantic vectors are obtained, present results of experimental analysis, and validated these with Amazon Mechanical Turk and a set of algorithms. To conclude, we discuss the strengths and weaknesses of the methodology. Copyright 2010 ACM.es of the methodology. Copyright 2010 ACM.